Overview

Dataset statistics

Number of variables18
Number of observations333
Missing cells620
Missing cells (%)10.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory44.7 KiB
Average record size in memory137.4 B

Variable types

Numeric13
Text4
Boolean1

Alerts

commercial_property is highly overall correlated with crime_rate and 8 other fieldsHigh correlation
competitor_density is highly overall correlated with is_testHigh correlation
crime_rate is highly overall correlated with commercial_property and 8 other fieldsHigh correlation
household_affluency is highly overall correlated with commercial_property and 8 other fieldsHigh correlation
household_size is highly overall correlated with household_affluency and 2 other fieldsHigh correlation
is_test is highly overall correlated with commercial_property and 4 other fieldsHigh correlation
normalised_sales is highly overall correlated with commercial_property and 7 other fieldsHigh correlation
property_value is highly overall correlated with commercial_property and 6 other fieldsHigh correlation
proportion_flats is highly overall correlated with commercial_property and 4 other fieldsHigh correlation
proportion_newbuilds is highly overall correlated with commercial_property and 7 other fieldsHigh correlation
proportion_nonretail is highly overall correlated with commercial_property and 8 other fieldsHigh correlation
public_transport_dist is highly overall correlated with commercial_property and 6 other fieldsHigh correlation
school_proximity is highly overall correlated with normalised_sales and 1 other fieldsHigh correlation
is_test is highly imbalanced (76.2%)Imbalance
location_id has 13 (3.9%) missing valuesMissing
crime_rate has 13 (3.9%) missing valuesMissing
proportion_flats has 13 (3.9%) missing valuesMissing
proportion_nonretail has 13 (3.9%) missing valuesMissing
new_store has 13 (3.9%) missing valuesMissing
commercial_property has 42 (12.6%) missing valuesMissing
household_size has 13 (3.9%) missing valuesMissing
proportion_newbuilds has 13 (3.9%) missing valuesMissing
public_transport_dist has 13 (3.9%) missing valuesMissing
transport_availability has 13 (3.9%) missing valuesMissing
property_value has 13 (3.9%) missing valuesMissing
school_proximity has 76 (22.8%) missing valuesMissing
competitor_density has 13 (3.9%) missing valuesMissing
household_affluency has 13 (3.9%) missing valuesMissing
normalised_sales has 13 (3.9%) missing valuesMissing
county has 13 (3.9%) missing valuesMissing
location_id,crime_rate,proportion_flats,proportion_nonretail,new_store,commercial_property,household_size,proportion_newbuilds,public_transport_dist,transport_availability,property_value,school_proximity,competitor_density,household_affluency,county has 320 (96.1%) missing valuesMissing
proportion_flats has 238 (71.5%) zerosZeros
proportion_newbuilds has 23 (6.9%) zerosZeros

Reproduction

Analysis started2024-02-09 10:50:04.687750
Analysis finished2024-02-09 10:50:18.567780
Duration13.88 seconds
Software versionydata-profiling vv4.6.4
Download configurationconfig.json

Variables

location_id
Real number (ℝ)

MISSING 

Distinct320
Distinct (%)100.0%
Missing13
Missing (%)3.9%
Infinite0
Infinite (%)0.0%
Mean252.3875
Minimum1
Maximum506
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2024-02-09T10:50:18.641735image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile26.95
Q1126.5
median251.5
Q3377.25
95-th percentile474.05
Maximum506
Range505
Interquartile range (IQR)250.75

Descriptive statistics

Standard deviation145.60058
Coefficient of variation (CV)0.576893
Kurtosis-1.1937793
Mean252.3875
Median Absolute Deviation (MAD)126
Skewness-0.021020651
Sum80764
Variance21199.53
MonotonicityNot monotonic
2024-02-09T10:50:18.765705image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
296 1
 
0.3%
315 1
 
0.3%
23 1
 
0.3%
415 1
 
0.3%
56 1
 
0.3%
195 1
 
0.3%
351 1
 
0.3%
111 1
 
0.3%
328 1
 
0.3%
392 1
 
0.3%
Other values (310) 310
93.1%
(Missing) 13
 
3.9%
ValueCountFrequency (%)
1 1
0.3%
3 1
0.3%
4 1
0.3%
5 1
0.3%
7 1
0.3%
10 1
0.3%
11 1
0.3%
13 1
0.3%
15 1
0.3%
17 1
0.3%
ValueCountFrequency (%)
506 1
0.3%
504 1
0.3%
503 1
0.3%
501 1
0.3%
500 1
0.3%
498 1
0.3%
494 1
0.3%
491 1
0.3%
489 1
0.3%
488 1
0.3%

crime_rate
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct319
Distinct (%)99.7%
Missing13
Missing (%)3.9%
Infinite0
Infinite (%)0.0%
Mean3.5963745
Minimum0.0071416
Maximum51.693093
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2024-02-09T10:50:19.043374image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0.0071416
5-th percentile0.028051685
Q10.0879366
median0.28968115
Q34.0635534
95-th percentile17.156496
Maximum51.693093
Range51.685951
Interquartile range (IQR)3.9756168

Descriptive statistics

Standard deviation7.1763415
Coefficient of variation (CV)1.9954377
Kurtosis13.231406
Mean3.5963745
Median Absolute Deviation (MAD)0.2520239
Skewness3.2555291
Sum1150.8399
Variance51.499878
MonotonicityNot monotonic
2024-02-09T10:50:19.170056image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0169613 2
 
0.6%
2.614707 1
 
0.3%
20.435598 1
 
0.3%
0.7208948 1
 
0.3%
0.0494827 1
 
0.3%
7.3887988 1
 
0.3%
0.1486854 1
 
0.3%
0.2419217 1
 
0.3%
9.3203417 1
 
0.3%
0.2770986 1
 
0.3%
Other values (309) 309
92.8%
(Missing) 13
 
3.9%
ValueCountFrequency (%)
0.0071416 1
0.3%
0.0123848 1
0.3%
0.0147013 1
0.3%
0.0148143 1
0.3%
0.015368 1
0.3%
0.0161816 1
0.3%
0.0162607 1
0.3%
0.0169613 2
0.6%
0.0200914 1
0.3%
0.021131 1
0.3%
ValueCountFrequency (%)
51.693093 1
0.3%
43.337534 1
0.3%
42.557947 1
0.3%
32.381054 1
0.3%
29.312878 1
0.3%
28.302093 1
0.3%
28.025921 1
0.3%
27.564994 1
0.3%
25.534723 1
0.3%
24.917743 1
0.3%

proportion_flats
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct25
Distinct (%)7.8%
Missing13
Missing (%)3.9%
Infinite0
Infinite (%)0.0%
Mean10.673438
Minimum0
Maximum100
Zeros238
Zeros (%)71.5%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2024-02-09T10:50:19.281087image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q312.5
95-th percentile75.25
Maximum100
Range100
Interquartile range (IQR)12.5

Descriptive statistics

Standard deviation22.579232
Coefficient of variation (CV)2.1154602
Kurtosis4.8618828
Mean10.673438
Median Absolute Deviation (MAD)0
Skewness2.3679581
Sum3415.5
Variance509.82171
MonotonicityNot monotonic
2024-02-09T10:50:19.390936image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
0 238
71.5%
20 13
 
3.9%
80 7
 
2.1%
22 7
 
2.1%
25 7
 
2.1%
12.5 6
 
1.8%
45 5
 
1.5%
75 3
 
0.9%
55 3
 
0.9%
21 3
 
0.9%
Other values (15) 28
 
8.4%
(Missing) 13
 
3.9%
ValueCountFrequency (%)
0 238
71.5%
12.5 6
 
1.8%
17.5 1
 
0.3%
18 1
 
0.3%
20 13
 
3.9%
21 3
 
0.9%
22 7
 
2.1%
25 7
 
2.1%
28 2
 
0.6%
30 3
 
0.9%
ValueCountFrequency (%)
100 1
 
0.3%
95 3
0.9%
90 2
 
0.6%
85 2
 
0.6%
82.5 1
 
0.3%
80 7
2.1%
75 3
0.9%
60 3
0.9%
55 3
0.9%
52.5 1
 
0.3%

proportion_nonretail
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct67
Distinct (%)20.9%
Missing13
Missing (%)3.9%
Infinite0
Infinite (%)0.0%
Mean11.307906
Minimum0.74
Maximum27.74
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2024-02-09T10:50:19.513844image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0.74
5-th percentile2.18
Q15.13
median9.9
Q318.1
95-th percentile21.89
Maximum27.74
Range27
Interquartile range (IQR)12.97

Descriptive statistics

Standard deviation7.0326934
Coefficient of variation (CV)0.6219271
Kurtosis-1.2360987
Mean11.307906
Median Absolute Deviation (MAD)6.615
Skewness0.28816142
Sum3618.53
Variance49.458776
MonotonicityNot monotonic
2024-02-09T10:50:19.641156image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18.1 84
25.2%
19.58 20
 
6.0%
6.2 13
 
3.9%
8.14 12
 
3.6%
21.89 10
 
3.0%
9.9 9
 
2.7%
3.97 7
 
2.1%
4.05 7
 
2.1%
5.86 7
 
2.1%
8.56 7
 
2.1%
Other values (57) 144
43.2%
(Missing) 13
 
3.9%
ValueCountFrequency (%)
0.74 1
0.3%
1.21 1
0.3%
1.22 1
0.3%
1.25 1
0.3%
1.32 1
0.3%
1.38 1
0.3%
1.47 1
0.3%
1.52 2
0.6%
1.69 1
0.3%
1.76 1
0.3%
ValueCountFrequency (%)
27.74 4
 
1.2%
25.65 6
 
1.8%
21.89 10
 
3.0%
19.58 20
 
6.0%
18.1 84
25.2%
15.04 2
 
0.6%
13.92 3
 
0.9%
13.89 1
 
0.3%
12.83 4
 
1.2%
11.93 4
 
1.2%

new_store
Text

MISSING 

Distinct2
Distinct (%)0.6%
Missing13
Missing (%)3.9%
Memory size2.7 KiB
2024-02-09T10:50:19.711762image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length3
Median length2
Mean length2.059375
Min length2

Characters and Unicode

Total characters659
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowno
2nd rowno
3rd rowno
4th rowno
5th rowno
ValueCountFrequency (%)
no 301
94.1%
yes 19
 
5.9%
2024-02-09T10:50:19.883426image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n 301
45.7%
o 301
45.7%
y 19
 
2.9%
e 19
 
2.9%
s 19
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 659
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 301
45.7%
o 301
45.7%
y 19
 
2.9%
e 19
 
2.9%
s 19
 
2.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 659
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 301
45.7%
o 301
45.7%
y 19
 
2.9%
e 19
 
2.9%
s 19
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 659
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 301
45.7%
o 301
45.7%
y 19
 
2.9%
e 19
 
2.9%
s 19
 
2.9%

commercial_property
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct76
Distinct (%)26.1%
Missing42
Missing (%)12.6%
Infinite0
Infinite (%)0.0%
Mean16.868557
Minimum1.75
Maximum1009
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2024-02-09T10:50:20.005127image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1.75
5-th percentile3.1
Q15.45
median9.4
Q314.05
95-th percentile21
Maximum1009
Range1007.25
Interquartile range (IQR)8.6

Descriptive statistics

Standard deviation73.806051
Coefficient of variation (CV)4.3753625
Kurtosis149.49455
Mean16.868557
Median Absolute Deviation (MAD)4.3
Skewness12.087634
Sum4908.75
Variance5447.3332
MonotonicityNot monotonic
2024-02-09T10:50:20.133908image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18.15 11
 
3.3%
9.4 11
 
3.3%
13.7 10
 
3.0%
26.05 10
 
3.0%
4.35 10
 
3.0%
12.75 9
 
2.7%
6.95 9
 
2.7%
9.7 8
 
2.4%
4.05 7
 
2.1%
17.5 7
 
2.1%
Other values (66) 199
59.8%
(Missing) 42
 
12.6%
ValueCountFrequency (%)
1.75 1
 
0.3%
1.95 1
 
0.3%
2.55 3
0.9%
2.65 1
 
0.3%
2.7 1
 
0.3%
2.75 1
 
0.3%
2.95 2
0.6%
3 1
 
0.3%
3.05 4
1.2%
3.15 2
0.6%
ValueCountFrequency (%)
1009 1
 
0.3%
767 1
 
0.3%
123 1
 
0.3%
26.05 10
3.0%
21 5
1.5%
19.5 7
2.1%
18.4 3
 
0.9%
18.15 11
3.3%
17.5 7
2.1%
17.15 6
1.8%

household_size
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct298
Distinct (%)93.1%
Missing13
Missing (%)3.9%
Infinite0
Infinite (%)0.0%
Mean3.2528031
Minimum0.561
Maximum5.725
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2024-02-09T10:50:20.252884image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0.561
5-th percentile2.18445
Q12.87975
median3.1975
Q33.59725
95-th percentile4.47095
Maximum5.725
Range5.164
Interquartile range (IQR)0.7175

Descriptive statistics

Standard deviation0.69544191
Coefficient of variation (CV)0.21379772
Kurtosis1.9956769
Mean3.2528031
Median Absolute Deviation (MAD)0.334
Skewness0.18421453
Sum1040.897
Variance0.48363944
MonotonicityNot monotonic
2024-02-09T10:50:20.373248image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.229 3
 
0.9%
3.03 2
 
0.6%
3.127 2
 
0.6%
3.122 2
 
0.6%
3.211 2
 
0.6%
3.417 2
 
0.6%
3.185 2
 
0.6%
2.304 2
 
0.6%
2.713 2
 
0.6%
2.888 2
 
0.6%
Other values (288) 299
89.8%
(Missing) 13
 
3.9%
ValueCountFrequency (%)
0.561 1
0.3%
0.863 1
0.3%
1.138 1
0.3%
1.368 1
0.3%
1.519 1
0.3%
1.652 1
0.3%
1.906 1
0.3%
1.926 1
0.3%
1.963 1
0.3%
1.97 1
0.3%
ValueCountFrequency (%)
5.725 1
0.3%
5.375 1
0.3%
5.266 1
0.3%
5.259 1
0.3%
5.247 1
0.3%
5.04 1
0.3%
5.034 1
0.3%
4.929 1
0.3%
4.923 1
0.3%
4.853 1
0.3%

proportion_newbuilds
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct252
Distinct (%)78.8%
Missing13
Missing (%)3.9%
Infinite0
Infinite (%)0.0%
Mean31.849062
Minimum0
Maximum94
Zeros23
Zeros (%)6.9%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2024-02-09T10:50:20.494749image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q16.35
median23.4
Q354.45
95-th percentile82.205
Maximum94
Range94
Interquartile range (IQR)48.1

Descriptive statistics

Standard deviation27.845777
Coefficient of variation (CV)0.87430443
Kurtosis-0.94057168
Mean31.849062
Median Absolute Deviation (MAD)19.95
Skewness0.58915328
Sum10191.7
Variance775.38727
MonotonicityNot monotonic
2024-02-09T10:50:20.619641image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 23
 
6.9%
4 4
 
1.2%
4.6 3
 
0.9%
23.5 3
 
0.9%
1.2 3
 
0.9%
4.4 3
 
0.9%
78.6 3
 
0.9%
2.7 3
 
0.9%
20.1 2
 
0.6%
29.6 2
 
0.6%
Other values (242) 271
81.4%
(Missing) 13
 
3.9%
ValueCountFrequency (%)
0 23
6.9%
0.7 1
 
0.3%
1.1 1
 
0.3%
1.2 3
 
0.9%
1.3 1
 
0.3%
1.5 1
 
0.3%
1.6 2
 
0.6%
1.8 2
 
0.6%
1.9 1
 
0.3%
2 1
 
0.3%
ValueCountFrequency (%)
94 1
0.3%
93.8 1
0.3%
93.5 1
0.3%
93.4 1
0.3%
92.2 2
0.6%
91.6 1
0.3%
91.1 1
0.3%
90.2 1
0.3%
90.1 1
0.3%
87 1
0.3%

public_transport_dist
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct286
Distinct (%)89.4%
Missing13
Missing (%)3.9%
Infinite0
Infinite (%)0.0%
Mean3.718765
Minimum1.137
Maximum10.7103
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2024-02-09T10:50:20.741052image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1.137
5-th percentile1.50955
Q12.138075
median3.09575
Q35.1167
95-th percentile7.66147
Maximum10.7103
Range9.5733
Interquartile range (IQR)2.978625

Descriptive statistics

Standard deviation1.9847652
Coefficient of variation (CV)0.53371623
Kurtosis0.14082632
Mean3.718765
Median Absolute Deviation (MAD)1.1629
Skewness0.94971955
Sum1190.0048
Variance3.9392931
MonotonicityNot monotonic
2024-02-09T10:50:20.862834image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.8122 3
 
0.9%
3.6519 3
 
0.9%
5.2873 3
 
0.9%
5.4007 3
 
0.9%
6.4798 3
 
0.9%
6.8147 3
 
0.9%
6.0622 2
 
0.6%
3.2721 2
 
0.6%
7.3967 2
 
0.6%
6.4584 2
 
0.6%
Other values (276) 294
88.3%
(Missing) 13
 
3.9%
ValueCountFrequency (%)
1.137 1
0.3%
1.1691 1
0.3%
1.1742 1
0.3%
1.2024 1
0.3%
1.2852 1
0.3%
1.3216 1
0.3%
1.3325 1
0.3%
1.3449 1
0.3%
1.358 1
0.3%
1.4191 1
0.3%
ValueCountFrequency (%)
10.7103 1
0.3%
9.2229 1
0.3%
9.1876 1
0.3%
9.0892 1
0.3%
8.9067 1
0.3%
8.7921 1
0.3%
8.6966 1
0.3%
8.5353 1
0.3%
8.344 1
0.3%
8.3248 1
0.3%
Distinct5
Distinct (%)1.6%
Missing13
Missing (%)3.9%
Memory size2.7 KiB
2024-02-09T10:50:20.966676image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length25
Median length22
Mean length21.865625
Min length20

Characters and Unicode

Total characters6997
Distinct characters19
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAll transport options
2nd rowAverage transport options
3rd rowMany transport options
4th rowNo transport options
5th rowAverage transport options
ValueCountFrequency (%)
transport 320
33.3%
options 320
33.3%
all 84
 
8.8%
average 72
 
7.5%
few 69
 
7.2%
no 53
 
5.5%
many 42
 
4.4%
2024-02-09T10:50:21.176716image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 1013
14.5%
t 960
13.7%
r 712
10.2%
n 682
9.7%
640
9.1%
s 640
9.1%
p 640
9.1%
a 434
6.2%
i 320
 
4.6%
e 213
 
3.0%
Other values (9) 743
10.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 6037
86.3%
Space Separator 640
 
9.1%
Uppercase Letter 320
 
4.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 1013
16.8%
t 960
15.9%
r 712
11.8%
n 682
11.3%
s 640
10.6%
p 640
10.6%
a 434
7.2%
i 320
 
5.3%
e 213
 
3.5%
l 168
 
2.8%
Other values (4) 255
 
4.2%
Uppercase Letter
ValueCountFrequency (%)
A 156
48.8%
F 69
21.6%
N 53
 
16.6%
M 42
 
13.1%
Space Separator
ValueCountFrequency (%)
640
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 6357
90.9%
Common 640
 
9.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 1013
15.9%
t 960
15.1%
r 712
11.2%
n 682
10.7%
s 640
10.1%
p 640
10.1%
a 434
6.8%
i 320
 
5.0%
e 213
 
3.4%
l 168
 
2.6%
Other values (8) 575
9.0%
Common
ValueCountFrequency (%)
640
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6997
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 1013
14.5%
t 960
13.7%
r 712
10.2%
n 682
9.7%
640
9.1%
s 640
9.1%
p 640
9.1%
a 434
6.2%
i 320
 
4.6%
e 213
 
3.0%
Other values (9) 743
10.6%

property_value
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct58
Distinct (%)18.1%
Missing13
Missing (%)3.9%
Infinite0
Infinite (%)0.0%
Mean408.83438
Minimum188
Maximum711
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2024-02-09T10:50:21.305617image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum188
5-th percentile216
Q1277
median330
Q3666
95-th percentile666
Maximum711
Range523
Interquartile range (IQR)389

Descriptive statistics

Standard deviation170.88897
Coefficient of variation (CV)0.41799072
Kurtosis-1.1841982
Mean408.83438
Median Absolute Deviation (MAD)79.5
Skewness0.63298263
Sum130827
Variance29203.041
MonotonicityNot monotonic
2024-02-09T10:50:21.438987image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
666 84
25.2%
307 25
 
7.5%
403 20
 
6.0%
437 10
 
3.0%
304 9
 
2.7%
398 9
 
2.7%
224 8
 
2.4%
296 8
 
2.4%
384 7
 
2.1%
264 7
 
2.1%
Other values (48) 133
39.9%
(Missing) 13
 
3.9%
ValueCountFrequency (%)
188 6
1.8%
193 6
1.8%
198 1
 
0.3%
216 4
1.2%
222 5
1.5%
223 3
 
0.9%
224 8
2.4%
226 1
 
0.3%
233 6
1.8%
241 1
 
0.3%
ValueCountFrequency (%)
711 4
 
1.2%
666 84
25.2%
469 1
 
0.3%
437 10
 
3.0%
432 6
 
1.8%
430 2
 
0.6%
422 1
 
0.3%
411 1
 
0.3%
403 20
 
6.0%
402 1
 
0.3%

school_proximity
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct40
Distinct (%)15.6%
Missing76
Missing (%)22.8%
Infinite0
Infinite (%)0.0%
Mean18.589494
Minimum13
Maximum21.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2024-02-09T10:50:21.727000image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile14.7
Q117.4
median19.1
Q320.2
95-th percentile21
Maximum21.2
Range8.2
Interquartile range (IQR)2.8

Descriptive statistics

Standard deviation2.0755286
Coefficient of variation (CV)0.11165062
Kurtosis-0.12017046
Mean18.589494
Median Absolute Deviation (MAD)1.1
Skewness-0.87250548
Sum4777.5
Variance4.3078189
MonotonicityNot monotonic
2024-02-09T10:50:21.833823image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
20.2 77
23.1%
14.7 14
 
4.2%
21 13
 
3.9%
18.4 11
 
3.3%
19.1 10
 
3.0%
17.8 10
 
3.0%
18.6 9
 
2.7%
17.4 9
 
2.7%
21.2 9
 
2.7%
16.6 9
 
2.7%
Other values (30) 86
25.8%
(Missing) 76
22.8%
ValueCountFrequency (%)
13 6
1.8%
13.6 1
 
0.3%
14.7 14
4.2%
14.9 1
 
0.3%
15.1 1
 
0.3%
15.2 7
2.1%
15.3 2
 
0.6%
15.6 2
 
0.6%
15.9 1
 
0.3%
16 3
 
0.9%
ValueCountFrequency (%)
21.2 9
 
2.7%
21.1 1
 
0.3%
21 13
 
3.9%
20.9 6
 
1.8%
20.2 77
23.1%
20.1 3
 
0.9%
19.7 4
 
1.2%
19.6 3
 
0.9%
19.2 7
 
2.1%
19.1 10
 
3.0%

competitor_density
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct226
Distinct (%)70.6%
Missing13
Missing (%)3.9%
Infinite0
Infinite (%)0.0%
Mean359.65756
Minimum3.5
Maximum396.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2024-02-09T10:50:21.952438image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum3.5
5-th percentile97.889
Q1376.7225
median392.205
Q3396.3525
95-th percentile396.9
Maximum396.9
Range393.4
Interquartile range (IQR)19.63

Descriptive statistics

Standard deviation86.048632
Coefficient of variation (CV)0.23925156
Kurtosis7.9850234
Mean359.65756
Median Absolute Deviation (MAD)4.695
Skewness-2.9883666
Sum115090.42
Variance7404.3671
MonotonicityNot monotonic
2024-02-09T10:50:22.089804image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
396.9 79
 
23.7%
395.24 3
 
0.9%
396.21 2
 
0.6%
377.07 2
 
0.6%
393.37 2
 
0.6%
395.11 2
 
0.6%
395.56 2
 
0.6%
395.63 2
 
0.6%
393.23 2
 
0.6%
389.71 2
 
0.6%
Other values (216) 222
66.7%
(Missing) 13
 
3.9%
ValueCountFrequency (%)
3.5 1
0.3%
3.65 1
0.3%
7.68 1
0.3%
9.32 1
0.3%
18.82 1
0.3%
22.01 1
0.3%
27.25 1
0.3%
43.06 1
0.3%
48.45 1
0.3%
50.92 1
0.3%
ValueCountFrequency (%)
396.9 79
23.7%
396.42 1
 
0.3%
396.33 1
 
0.3%
396.3 1
 
0.3%
396.28 1
 
0.3%
396.24 1
 
0.3%
396.21 2
 
0.6%
396.14 1
 
0.3%
396.06 1
 
0.3%
395.99 1
 
0.3%

household_affluency
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct298
Distinct (%)93.1%
Missing13
Missing (%)3.9%
Infinite0
Infinite (%)0.0%
Mean3.1440078
Minimum0.4325
Maximum9.4925
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2024-02-09T10:50:22.221737image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0.4325
5-th percentile0.923625
Q11.80375
median2.80875
Q34.091875
95-th percentile6.693125
Maximum9.4925
Range9.06
Interquartile range (IQR)2.288125

Descriptive statistics

Standard deviation1.7740414
Coefficient of variation (CV)0.56426114
Kurtosis0.77003274
Mean3.1440078
Median Absolute Deviation (MAD)1.125
Skewness0.98958754
Sum1006.0825
Variance3.1472231
MonotonicityNot monotonic
2024-02-09T10:50:22.346219image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.5325 3
 
0.9%
5.995 2
 
0.6%
1.1125 2
 
0.6%
1.9 2
 
0.6%
1.42 2
 
0.6%
1.645 2
 
0.6%
2.025 2
 
0.6%
1.375 2
 
0.6%
1.9475 2
 
0.6%
1.3325 2
 
0.6%
Other values (288) 299
89.8%
(Missing) 13
 
3.9%
ValueCountFrequency (%)
0.4325 1
0.3%
0.495 1
0.3%
0.7175 1
0.3%
0.72 1
0.3%
0.735 1
0.3%
0.74 1
0.3%
0.7525 1
0.3%
0.7825 1
0.3%
0.79 2
0.6%
0.815 1
0.3%
ValueCountFrequency (%)
9.4925 1
0.3%
9.245 1
0.3%
8.6925 1
0.3%
8.6025 1
0.3%
7.9975 1
0.3%
7.6575 1
0.3%
7.6475 1
0.3%
7.42 1
0.3%
7.3875 1
0.3%
7.3825 1
0.3%

normalised_sales
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct188
Distinct (%)58.8%
Missing13
Missing (%)3.9%
Infinite0
Infinite (%)0.0%
Mean-0.016966731
Minimum-1.936974
Maximum2.9684773
Zeros0
Zeros (%)0.0%
Negative187
Negative (%)56.2%
Memory size2.7 KiB
2024-02-09T10:50:22.469843image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-1.936974
5-th percentile-1.3374188
Q1-0.58524963
median-0.14375902
Q30.24322658
95-th percentile2.1852402
Maximum2.9684773
Range4.9054512
Interquartile range (IQR)0.82847621

Descriptive statistics

Standard deviation0.97856136
Coefficient of variation (CV)-57.675302
Kurtosis1.6704039
Mean-0.016966731
Median Absolute Deviation (MAD)0.4033371
Skewness1.1175046
Sum-5.4293541
Variance0.95758233
MonotonicityNot monotonic
2024-02-09T10:50:22.602079image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.96847726 10
 
3.0%
-0.2364175427 5
 
1.5%
-0.3781305781 5
 
1.5%
-0.3672295754 5
 
1.5%
0.1233155474 5
 
1.5%
-0.5416456191 5
 
1.5%
0.03610752556 5
 
1.5%
0.2432265774 5
 
1.5%
0.003404517369 4
 
1.2%
0.177820561 4
 
1.2%
Other values (178) 267
80.2%
(Missing) 13
 
3.9%
ValueCountFrequency (%)
-1.936973968 1
0.3%
-1.871567952 1
0.3%
-1.718953914 1
0.3%
-1.697151908 2
0.6%
-1.675349903 1
0.3%
-1.599042884 1
0.3%
-1.577240878 2
0.6%
-1.566339876 1
0.3%
-1.533636867 1
0.3%
-1.522735865 1
0.3%
ValueCountFrequency (%)
2.96847726 10
3.0%
2.804962219 1
 
0.3%
2.783160213 1
 
0.3%
2.53243715 1
 
0.3%
2.401625118 1
 
0.3%
2.259912082 1
 
0.3%
2.216308071 1
 
0.3%
2.183605063 1
 
0.3%
1.856574981 1
 
0.3%
1.649455929 1
 
0.3%

county
Text

MISSING 

Distinct98
Distinct (%)30.6%
Missing13
Missing (%)3.9%
Memory size2.7 KiB
2024-02-09T10:50:22.797573image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length4.046875
Min length3

Characters and Unicode

Total characters1295
Distinct characters12
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique30 ?
Unique (%)9.4%

Sample

1st rowc_40
2nd rowc_80
3rd rowc_53
4th rowc_65
5th rowc_97
ValueCountFrequency (%)
c_60 10
 
3.1%
c_61 10
 
3.1%
c_50 10
 
3.1%
c_45 9
 
2.8%
c_72 9
 
2.8%
c_68 8
 
2.5%
c_48 8
 
2.5%
c_39 7
 
2.2%
c_63 7
 
2.2%
c_62 7
 
2.2%
Other values (88) 235
73.4%
2024-02-09T10:50:23.113933image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
c 320
24.7%
_ 320
24.7%
6 89
 
6.9%
4 82
 
6.3%
5 80
 
6.2%
7 69
 
5.3%
3 67
 
5.2%
2 64
 
4.9%
1 55
 
4.2%
8 54
 
4.2%
Other values (2) 95
 
7.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 655
50.6%
Lowercase Letter 320
24.7%
Connector Punctuation 320
24.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
6 89
13.6%
4 82
12.5%
5 80
12.2%
7 69
10.5%
3 67
10.2%
2 64
9.8%
1 55
8.4%
8 54
8.2%
9 52
7.9%
0 43
6.6%
Lowercase Letter
ValueCountFrequency (%)
c 320
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 320
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 975
75.3%
Latin 320
 
24.7%

Most frequent character per script

Common
ValueCountFrequency (%)
_ 320
32.8%
6 89
 
9.1%
4 82
 
8.4%
5 80
 
8.2%
7 69
 
7.1%
3 67
 
6.9%
2 64
 
6.6%
1 55
 
5.6%
8 54
 
5.5%
9 52
 
5.3%
Latin
ValueCountFrequency (%)
c 320
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1295
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
c 320
24.7%
_ 320
24.7%
6 89
 
6.9%
4 82
 
6.3%
5 80
 
6.2%
7 69
 
5.3%
3 67
 
5.2%
2 64
 
4.9%
1 55
 
4.2%
8 54
 
4.2%
Other values (2) 95
 
7.3%

is_test
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size461.0 B
False
320 
True
 
13
ValueCountFrequency (%)
False 320
96.1%
True 13
 
3.9%
2024-02-09T10:50:23.225093image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Distinct13
Distinct (%)100.0%
Missing320
Missing (%)96.1%
Memory size2.7 KiB
2024-02-09T10:50:23.346783image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length135
Median length131
Mean length124.76923
Min length100

Characters and Unicode

Total characters1622
Distinct characters33
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13 ?
Unique (%)100.0%

Sample

1st row105,0.03996809999999999,34.0,6.09,no,4.150000000000001,3.59,59.6,5.4917,Many transport options,329,16.1,395.75,2.375,c_42
2nd row400,0.5877581999999999,20.0,3.97,no,14.850000000000001,5.398,8.5,2.2885,Average transport options,264,13.0,386.86,1.4775,c_140
3rd row338,1.1169258999999998,0.0,8.14,no,9.399999999999997,2.8129999999999997,0.0,4.0952,Few transport options,307,,394.54,4.97,c_55
4th row227,1.5174092,0.0,19.58,no,12.75,3.066,0.0,1.7573,Average transport options,403,14.7,353.89,1.6075,c_62
5th row114,83.093533,0.0,18.1,no,16.450000000000003,2.9570000000000007,0.0,1.8026,All transport options,666,20.2,16.45,5.155000000000001,c_22
ValueCountFrequency (%)
transport 13
33.3%
105,0.03996809999999999,34.0,6.09,no,4.150000000000001,3.59,59.6,5.4917,many 1
 
2.6%
options,233,17.9,383.37,1.4525,c_63 1
 
2.6%
363,0.1482221,0.0,8.56,no,,3.1270000000000007,14.799999999999997,2.1224,average 1
 
2.6%
options,307,17.4,385.91,0.6175,c_122 1
 
2.6%
148,0.6500776999999999,0.0,6.2,no,7.850000000000001,5.337,26.700000000000003,3.8384,many 1
 
2.6%
options,666,,385.09,4.3175,c_56 1
 
2.6%
136,5.751892099999999,0.0,18.1,no,18.15,3.2970000000000006,8.200000000000003,2.3682,all 1
 
2.6%
options,287,19.6,393.68,1.27,c_58 1
 
2.6%
341,0.2168018,0.0,7.38,no,7.15,3.431,85.3,5.4159,average 1
 
2.6%
Other values (17) 17
43.6%
2024-02-09T10:50:23.609036image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 235
14.5%
9 225
13.9%
, 182
11.2%
. 127
 
7.8%
1 83
 
5.1%
3 73
 
4.5%
2 70
 
4.3%
5 66
 
4.1%
8 61
 
3.8%
7 60
 
3.7%
Other values (23) 440
27.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 971
59.9%
Other Punctuation 309
 
19.1%
Lowercase Letter 290
 
17.9%
Space Separator 26
 
1.6%
Connector Punctuation 13
 
0.8%
Uppercase Letter 13
 
0.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 53
18.3%
n 40
13.8%
t 39
13.4%
r 30
10.3%
s 27
9.3%
p 26
9.0%
a 19
 
6.6%
i 13
 
4.5%
c 13
 
4.5%
e 10
 
3.4%
Other values (5) 20
 
6.9%
Decimal Number
ValueCountFrequency (%)
0 235
24.2%
9 225
23.2%
1 83
 
8.5%
3 73
 
7.5%
2 70
 
7.2%
5 66
 
6.8%
8 61
 
6.3%
7 60
 
6.2%
6 55
 
5.7%
4 43
 
4.4%
Uppercase Letter
ValueCountFrequency (%)
A 8
61.5%
M 2
 
15.4%
N 2
 
15.4%
F 1
 
7.7%
Other Punctuation
ValueCountFrequency (%)
, 182
58.9%
. 127
41.1%
Space Separator
ValueCountFrequency (%)
26
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 13
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1319
81.3%
Latin 303
 
18.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 53
17.5%
n 40
13.2%
t 39
12.9%
r 30
9.9%
s 27
8.9%
p 26
8.6%
a 19
 
6.3%
i 13
 
4.3%
c 13
 
4.3%
e 10
 
3.3%
Other values (9) 33
10.9%
Common
ValueCountFrequency (%)
0 235
17.8%
9 225
17.1%
, 182
13.8%
. 127
9.6%
1 83
 
6.3%
3 73
 
5.5%
2 70
 
5.3%
5 66
 
5.0%
8 61
 
4.6%
7 60
 
4.5%
Other values (4) 137
10.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1622
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 235
14.5%
9 225
13.9%
, 182
11.2%
. 127
 
7.8%
1 83
 
5.1%
3 73
 
4.5%
2 70
 
4.3%
5 66
 
4.1%
8 61
 
3.8%
7 60
 
3.7%
Other values (23) 440
27.1%

Interactions

2024-02-09T10:50:17.078612image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:05.183791image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:06.202319image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:07.152718image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:08.119247image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:09.197587image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:10.140008image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:11.061006image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:11.997042image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:13.075683image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:14.069292image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:14.960661image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:16.133631image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:17.151898image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:05.305170image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:06.275661image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:07.226002image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:08.192715image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:09.270530image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:10.211187image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:11.133655image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:12.213600image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:13.154270image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:14.139040image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:15.039208image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:16.204896image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:17.226409image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:05.379589image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:06.349039image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:07.299623image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:08.268496image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:09.342140image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:10.280440image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:11.203534image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:12.283512image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:13.247447image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:14.206752image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:15.117639image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:16.275348image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:17.302470image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:05.455222image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:06.422889image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:07.372151image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:08.340764image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:09.416933image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:10.352412image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:11.274304image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:12.355001image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:13.320850image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:14.275792image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:15.197010image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:16.347261image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:17.377149image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:05.534023image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:06.497785image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:07.446292image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:08.552821image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:09.489426image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:10.423450image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:11.345263image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:12.425891image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:13.397180image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:14.345956image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:15.274824image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:16.421816image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:17.453939image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:05.607553image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:06.570676image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:07.522478image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:08.622669image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:09.564804image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:10.495606image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:11.417241image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:12.498327image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:13.469749image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:14.415995image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:15.353150image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:16.495926image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:17.524896image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:05.681720image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:06.641949image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:07.593678image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:08.691333image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:09.634625image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:10.562774image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:11.485194image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:12.568039image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:13.541261image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:14.483197image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:15.429993image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:16.567661image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:17.596437image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:05.753037image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:06.712341image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:07.665461image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:08.759530image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:09.705318image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:10.631439image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:11.555067image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:12.640726image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:13.612517image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:14.548498image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:15.508322image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:16.638826image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:17.670223image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:05.826672image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:06.783828image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:07.739439image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:08.831767image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:09.775057image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:10.698557image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:11.626946image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:12.707776image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:13.689591image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:14.616448image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:15.584687image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:16.710610image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:17.746996image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:05.904230image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:06.857114image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:07.816545image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:08.907652image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:09.848295image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:10.770878image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:11.701138image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:12.782302image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:13.769028image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:14.685391image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:15.824470image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:16.784826image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:17.817295image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:05.971488image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:06.923977image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:07.884875image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:08.974047image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:09.915136image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:10.836007image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:11.766493image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:12.849921image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:13.839555image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:14.746713image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:15.894111image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:16.850254image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:17.902549image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:06.055545image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:07.007140image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:07.972120image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:09.055477image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:09.996362image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:10.921094image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:11.851733image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:12.933099image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:13.922937image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:14.826903image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:15.980680image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:16.936742image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:17.975188image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:06.127010image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:07.079590image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:08.042432image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:09.126117image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:10.067097image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:10.990811image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:11.920504image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:13.002572image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:13.995628image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:14.891205image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:16.054776image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:50:17.006441image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Correlations

2024-02-09T10:50:23.704677image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
commercial_propertycompetitor_densitycrime_ratehousehold_affluencyhousehold_sizeis_testlocation_idnormalised_salesproperty_valueproportion_flatsproportion_newbuildsproportion_nonretailpublic_transport_distschool_proximity
commercial_property1.000-0.2770.7790.612-0.3491.0000.013-0.5510.640-0.627-0.7880.756-0.8570.399
competitor_density-0.2771.000-0.328-0.2020.0491.0000.0420.145-0.2870.1570.210-0.2850.226-0.072
crime_rate0.779-0.3281.0000.640-0.3671.0000.059-0.5450.735-0.558-0.6840.716-0.7130.466
household_affluency0.612-0.2020.6401.000-0.6501.000-0.019-0.8620.536-0.482-0.6560.649-0.5800.436
household_size-0.3490.049-0.367-0.6501.0001.000-0.0900.641-0.3180.3930.301-0.4730.329-0.263
is_test1.0001.0001.0001.0001.0001.000NaNNaNNaNNaNNaNNaNNaNNaN
location_id0.0130.0420.059-0.019-0.090NaN1.0000.033-0.0110.035-0.0140.0140.0130.033
normalised_sales-0.5510.145-0.545-0.8620.641NaN0.0331.000-0.5460.4420.562-0.5860.456-0.527
property_value0.640-0.2870.7350.536-0.318NaN-0.011-0.5461.000-0.370-0.5350.658-0.5570.474
proportion_flats-0.6270.157-0.558-0.4820.393NaN0.0350.442-0.3701.0000.550-0.6330.611-0.451
proportion_newbuilds-0.7880.210-0.684-0.6560.301NaN-0.0140.562-0.5350.5501.000-0.6790.822-0.427
proportion_nonretail0.756-0.2850.7160.649-0.473NaN0.014-0.5860.658-0.633-0.6791.000-0.7470.500
public_transport_dist-0.8570.226-0.713-0.5800.329NaN0.0130.456-0.5570.6110.822-0.7471.000-0.374
school_proximity0.399-0.0720.4660.436-0.263NaN0.033-0.5270.474-0.451-0.4270.500-0.3741.000

Missing values

2024-02-09T10:50:18.095122image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-02-09T10:50:18.330892image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

location_idcrime_rateproportion_flatsproportion_nonretailnew_storecommercial_propertyhousehold_sizeproportion_newbuildspublic_transport_disttransport_availabilityproperty_valueschool_proximitycompetitor_densityhousehold_affluencynormalised_salescountyis_testlocation_id,crime_rate,proportion_flats,proportion_nonretail,new_store,commercial_property,household_size,proportion_newbuilds,public_transport_dist,transport_availability,property_value,school_proximity,competitor_density,household_affluency,county
0464.017.6005410.018.10noNaN2.92629.02.9084All transport options666.020.2368.744.5325-0.399933c_40FalseNaN
1504.00.60355620.03.97no14.854.52010.62.1398Average transport options264.013.0388.371.81502.216308c_80FalseNaN
2295.00.6068100.06.20no7.702.98131.93.6715Many transport options307.017.4378.352.91250.166920c_53FalseNaN
3187.00.01238555.02.25no1.953.45368.17.3073No transport options300.015.3394.722.0575-0.083804c_65FalseNaN
4193.00.016182100.01.32no3.053.81659.58.3248Average transport options256.015.1392.900.98750.962693c_97FalseNaN
5160.00.0686590.011.93no11.153.9769.02.1675No transport options273.021.0396.901.41000.123316c_69FalseNaN
643.00.25412612.57.87no8.703.3775.76.3467Average transport options311.015.2392.525.1125-0.846874c_22FalseNaN
7278.06.5811310.018.10no9.103.24235.33.4242All transport options666.020.2396.902.68500.025207c_54FalseNaN
8387.017.9221390.018.10no16.452.8964.61.9096All transport options666.020.27.686.0975-1.577241c_51FalseNaN
998.05.4377070.018.10no18.153.70110.02.5975All transport options666.020.2255.234.1050-0.694260c_47FalseNaN
location_idcrime_rateproportion_flatsproportion_nonretailnew_storecommercial_propertyhousehold_sizeproportion_newbuildspublic_transport_disttransport_availabilityproperty_valueschool_proximitycompetitor_densityhousehold_affluencynormalised_salescountyis_testlocation_id,crime_rate,proportion_flats,proportion_nonretail,new_store,commercial_property,household_size,proportion_newbuilds,public_transport_dist,transport_availability,property_value,school_proximity,competitor_density,household_affluency,county
323NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNTrue227,1.5174092,0.0,19.58,no,12.75,3.066,0.0,1.7573,Average transport options,403,14.7,353.89,1.6075,c_62
324NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNTrue114,83.093533,0.0,18.1,no,16.450000000000003,2.9570000000000007,0.0,1.8026,All transport options,666,20.2,16.45,5.155000000000001,c_22
325NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNTrue203,10.988323399999999,0.0,18.1,no,19.5,3.4060000000000006,2.799999999999997,2.0651,All transport options,666,20.2,385.96,4.88,c_19
326NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNTrue12,0.15989499999999998,0.0,6.91,no,4.899999999999999,3.1689999999999996,93.4,5.7209,No transport options,233,17.9,383.37,1.4525,c_63
327NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNTrue199,9.3419925,0.0,18.1,yes,15.899999999999997,2.875,10.400000000000006,1.1296,All transport options,666,20.2,347.88,2.22,c_107
328NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNTrue477,0.010237799999999998,90.0,2.97,no,2.500000000000002,4.087999999999999,79.2,7.3073,No transport options,285,15.3,394.72,1.9625,c_69
329NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNTrue341,0.2168018,0.0,7.38,no,7.15,3.431,85.3,5.4159,Average transport options,287,19.6,393.68,1.27,c_58
330NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNTrue136,5.751892099999999,0.0,18.1,no,18.15,3.2970000000000006,8.200000000000003,2.3682,All transport options,666,,385.09,4.3175,c_56
331NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNTrue148,0.6500776999999999,0.0,6.2,no,7.850000000000001,5.337,26.700000000000003,3.8384,Many transport options,307,17.4,385.91,0.6175,c_122
332NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNTrue363,0.1482221,0.0,8.56,no,,3.1270000000000007,14.799999999999997,2.1224,Average transport options,384,20.9,387.69,3.5225,c_63